Open AccessPARTICLE FILTER-BASED OBJECT TRACKING USING JOINT FEATURES OF COLOR AND LOCAL BINARY PATTERN HISTOGRAM FOURIER
Author(s) -
Dewa Made Wiharta
Publication year - 2016
Publication title -
jurnal ilmiah kursor: menuju solusi teknologi informasi
Language(s) - English
Resource type - Journals
ISSN - 2301-6914
DOI - 10.28961/kursor.v8i2.64
Subject(s) - particle filter , artificial intelligence , computer vision , video tracking , histogram , tracking (education) , color histogram , computer science , feature (linguistics) , local binary patterns , pattern recognition (psychology) , filter (signal processing) , object model , object (grammar) , image processing , color image , image (mathematics) , psychology , pedagogy , linguistics , philosophy
Object tracking is defined as the problem of estimating object location in image sequences. In general, the problems of object tracking in real time and complex environtment are affected by many uncertainty. In this research we use a sequensial Monte Carlo method, known as particle filter, to build an object tracking algorithm. Particle filter, due to its multiple hypotheses, is known to be a robust method in object tracking task. The performances of particle filter is defined by how the particles distributed. The role of distribution is regulated by the system model being used. In this research, a modified system model is proposed to manage particles distribution to achieve better performance. Object representation also plays important role in object tracking. In this research, we combine color histogram and texture from Local Binary Pattern Histogram Fourier (LBPHF) operator as feature in object tracking. Our experiments show that the proposed system model delivers a more robust tracking task, especially for objects with sudden changes in speed and direction. The proposed joint feature is able to capture object with changing shape and has better accuracy than single feature of color or joint color texture from other LBP variants.